code: def create_defaults_and_kwargs_from_preprocessor_kwargs(
      self, preprocessor_kwargs
  ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
      if preprocessor_kwargs is None:
          preprocessor_kwargs = {}
      kwargs = copy.deepcopy(preprocessor_kwargs)
      defaults = {}
  
      pad_on_right = self.preprocessor.padding_side == "right"
      max_seq_length = min(self.preprocessor.model_max_length, 384)
      max_seq_length = kwargs.pop("max_length", max_seq_length)
      stride = min(max_seq_length // 2, 128)
  
      defaults["padding"] = kwargs.pop("padding", "max_length")
      defaults["truncation"] = kwargs.pop(
          "truncation", "only_second" if pad_on_right else "only_first"
      )
      defaults["max_length"] = max_seq_length
      defaults["stride"] = kwargs.pop("stride", stride)
      defaults["return_overflowing_tokens"] = kwargs.pop(
          "return_overflowing_tokens", False
      )
      defaults["return_offsets_mapping"] = kwargs.pop(
          "return_offsets_mapping", False
      )
      return defaults, kwargs
  
file_path: optimum/optimum/utils/preprocessing/question_answering.py
imports: import copy
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
  from transformers import PreTrainedTokenizerBase
  from .base import TaskProcessor
label_desc: Use image node_id 1 for calling the QuestionAnsweringProcessing obj's underlying member method code with example usage: obj.create_defaults_and_kwargs_from_preprocessor_kwargs(preprocessor_kwargs) and returns: defaults,  kwargs